A novel classification regression method for gridded electric power consumption estimation in China

doi:10.1038/s41598-020-75543-2

	A novel classification regression method for gridded electric power consumption estimation in China
	Chen, Mulin 1,2; Cai, Hongyan 1; Yang, Xiaohuan 1; Jin, Cui 3
刊名	SCIENTIFIC REPORTS
	2020-10-29
卷号	10 期号:1 页码:12
ISSN号	2045-2322
DOI	10.1038/s41598-020-75543-2
通讯作者	Cai, Hongyan(caihy@igsnrr.ac.cn)
英文摘要	Spatially explicit information on electric power consumption (EPC) is crucial for effective electricity allocation and utilization. Many studies have estimated fine-scale spatial EPC based on remotely sensed nighttime light (NTL). However, the spatial non-stationary relationship between EPC and NTL at prefectural level tends to be overlooked in existing literature. In this study, a classification regression method to estimate the gridded EPC in China based on imaging NTL via a Visible Infrared Imaging Radiometer Suite (VIIRS) was described. In addition, owing to some inherent omissions in the VIIRS NTL data, the study has employed the cubic Hermite interpolation to produce a more appropriate NTL dataset for estimation. The proposed method was compared with ordinary least squares (OLS) and geographically weighted regression (GWR) approaches. The results showed that our proposed method outperformed OLS and GWR in relative error (RE) and mean absolute percentage error (MAPE). The desirable results benefited mainly from a reasonable classification scheme that fully considered the spatial non-stationary relationship between EPC and NTL. Thus, the analysis suggested that the proposed classification regression method would enhance the accuracy of the gridded EPC estimation and provide a valuable reference predictive model for electricity consumption.
资助项目	Strategic Priority Research Program of Chinese Academy of Sciences[XDA20010203] ; National Key R&D Program of China[2018YFC1800103] ; National Natural Science Foundation of China[41771460]
WOS关键词	GEOGRAPHICALLY WEIGHTED REGRESSION ; NIGHTTIME LIGHT IMAGES ; GROSS DOMESTIC PRODUCT ; DMSP-OLS ; SPATIOTEMPORAL DYNAMICS ; POPULATION-DENSITY ; ENERGY-CONSUMPTION ; EXPANSION ; SPATIALISATION ; EMISSIONS
WOS研究方向	Science & Technology - Other Topics
语种	英语
出版者	NATURE RESEARCH
WOS记录号	WOS:000587689500021
资助机构	Strategic Priority Research Program of Chinese Academy of Sciences ; National Key R&D Program of China ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/156503]
专题	中国科学院地理科学与资源研究所
通讯作者	Cai, Hongyan
作者单位	1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, 11A,Datun Rd, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Liaoning Normal Univ, Coll Urban & Environm, Dalian, Peoples R China
推荐引用方式 GB/T 7714	Chen, Mulin,Cai, Hongyan,Yang, Xiaohuan,et al. A novel classification regression method for gridded electric power consumption estimation in China[J]. SCIENTIFIC REPORTS,2020,10(1):12.
APA	Chen, Mulin,Cai, Hongyan,Yang, Xiaohuan,&Jin, Cui.(2020).A novel classification regression method for gridded electric power consumption estimation in China.SCIENTIFIC REPORTS,10(1),12.
MLA	Chen, Mulin,et al."A novel classification regression method for gridded electric power consumption estimation in China".SCIENTIFIC REPORTS 10.1(2020):12.